- Lead high-impact machine learning and feature-development initiatives across device, network, browser, mobile, session, and behavioral intelligence.
- Own ambiguous fraud and identity risk problems where data quality, label reliability, adversarial behavior, customer impact, and product tradeoffs must be evaluated together.
- Develop production risk signals and models that balance fraud detection, false-positive risk, coverage, latency, explainability, robustness, and operational maintainability.
- Build and guide scalable feature-engineering approaches for high-cardinality, sparse, noisy, and platform-dependent telemetry.
- Investigate complex signal patterns such as spoofing, emulator behavior, automation, proxy/VPN usage, low-entropy fingerprints, telemetry gaps, device fragmentation, and over-linkage risk.
- Define evaluation methods for Digital Intelligence signals, including holdout design, leakage checks, drift monitoring, adversarial robustness, customer impact analysis, and long-term signal stability.
- Influence telemetry collection, data contracts, feature logging, model monitoring, and production readiness in partnership with engineering, product, risk, and platform teams.
- Translate open-ended product, customer, and fraud-risk questions into clear data science approaches, measurable hypotheses, and production-ready signal roadmaps.
- Raise team standards for feature quality, model validation, explainability, documentation, and risk-signal governance.
- Mentor data scientists by improving problem framing, modeling judgment, validation rigor, code quality, and ability to operate independently in ambiguous domains.
Requirements
- Master’s or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Data Science, or a related quantitative field.
- 12+ years of experience in data science, applied machine learning, statistical modeling, or related technical roles.
- Significant experience building, deploying, validating, and improving production machine learning models, risk signals, or decisioning systems.
- Strong background in fraud detection, identity verification, trust and safety, anomaly detection, cybersecurity, risk modeling, or another adversarial data domain.
- Expert-level SQL skills and extensive experience working with large-scale, complex, noisy datasets.
- Strong proficiency in Python and distributed data processing frameworks such as Spark, PySpark, or equivalent tools.
- Deep understanding of supervised learning, unsupervised learning, anomaly detection, feature engineering, model evaluation, production monitoring, and statistical validation.
- Demonstrated ability to work with imperfect labels, delayed outcomes, telemetry artifacts, instrumentation gaps, and changing fraud patterns.
- Strong judgment across data quality, modeling approach, feature design, explainability, operational complexity, and business impact.
- Excellent communication skills, including the ability to explain complex data science decisions and risk tradeoffs to technical and non-technical audiences.
Benefits
- Opportunity to influence telemetry, product direction, and data science standards while mentoring others.
- Meaningful ownership over ambiguous, high-impact technical problems, from signal strategy and evaluation design to production rollout and long-term signal quality.
Core Competencies
Candidates should emphasize their expertise in machine learning model development, fraud detection, and data science methodologies. Highlighting experience in mentoring teams, managing complex data challenges, and collaborating across engineering and product teams will be crucial.
Highest-signal resume keywords
- Machine Learning Model Development
- Fraud Detection
- Data Science Methodologies
- Mentoring Data Scientists
- Collaboration Across Teams
ATS Optimization Keywords
Hard Skills
- Machine Learning
- Statistical Modeling
- SQL
- Python
- Feature Engineering
Soft Skills
- Communication Skills
- Judgment
- Problem Framing
- Mentoring
- Collaboration
Certifications & Qualifications
- Master’s Degree
- Ph.D.
Industry Keywords
- Fraud Detection
- Identity Verification
- Cybersecurity
- Risk Modeling
- Anomaly Detection
Tools & Technologies
- Spark
- PySpark
- Data Processing Frameworks
- Telemetry Systems
- Model Monitoring Tools